Suppose you are looking for the best relational database management tools for your company: two big players are Tableau Software and the R programming language. However, these tools differ significantly. But is Tableau better than R?
Tableau is not better or worse than R. Both perform varying tasks and cater to different users. If you are unfamiliar with coding and looking for a way to present and share data simply, Tableau is better. On the other hand, programmers performing more tasks in greater detail should use R.
Read on to learn more about Tableau, R, and the areas in which Tableau succeeds and fails in comparison to R.
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Table of Contents
What is Tableau?
First thing’s first: what is Tableau? You probably know by now that Tableau is a relational database management tool. However, it is not a programming language. On the contrary, Tableau uses programming language in its software background to interpret and present data visually.
How does it work? Tableau runs a programming language called, VizQL, a companion language to the commonly used Structured Query Language. VizQL intakes SQL expressions, formulae created in SQL to ask the computer for specific data and outputs it as visual charts and dashboards.
In other words, Tableau software is a presentation tool made for those unfamiliar with regional databases or programming language in general. It makes data consumption and sharing accessible for the average user. The operation uses simple actions like dragging, dropping, and clicking.
What is R?
Unlike Tableau, R is a programming language. While it can make itself a relational database management tool, R’s origins are statistics applications. The R Project characterizes the language as an ‘environment’ or seamlessly operating system, not a group of inflexible tools like other data programs.
R focuses on three tasks: data manipulation, calculation, and graphical display. How does R accomplish these tasks? Its software suite includes a variety of products in the pursuit of these goals:
- Storage: R provides a storage facility used for handling data.
- Calculations: The R suite contains operators that calculate arrays in specified matrices.
- Analysis: There are plenty of intermediate-level tools available in the R suite for data analysis.
- Graphical display: R offers facilities to develop visual analysis and display whether the presentation is a virtual or physical copy.
- Programming language: As mentioned before, R is a programming language. It is a high-level language meant to communicate data tangibly via conditionals, loops, user-developed recursive language, and input/output spaces.
R benefits from being a very flexible programming language. R can incorporate other languages like C, C++, and Fortran on call-time for more complicated procedures. Advanced programmers can alter R objects directly by writing C code.
In addition to these features, R has eight packages in its suite that can extend its capabilities. There are even more packages available from the CRAN family of websites.
Compared to my Tableau briefing, there is a lot more to digest. What does it all mean? Tableau is a more visually oriented platform made for those unfamiliar with programming. R includes graphical functions like Tableau, but it is also a language that processes statistical data and is highly adaptable. R is a programmer’s tool. Tableau is not.
If you have followed our conversation so far, it may seem like a moot point to decide if Tableau or R is the better software. After all, they both target different user bases and accomplish various tasks. Realistically speaking, you can use both products in tandem. Let’s look at what makes Tableau better than R and what shortcomings would make R the better tool.
3 Areas Tableau is Better Than R
Tableau is all about access, which is the area in which it excels over R. This software is quick and easy to learn. Furthermore, its ease of operation requires practically no programming knowledge. R, on the other hand, has a much steeper learning curve. Compared to other products, like Excel, SPSS, SAS, and Python, R takes more time to use and has more incredible difficulty.
Gorgeous visualization is another boon of Tableau software over R. After all, the software’s goal is to present data clearly and attractively. This trait is essential when you need to submit data to a client or employee.
Ease of Operation
Additionally, creating visual interpretations of data is easier on Tableau. R has its visualization platform called Markdown. Like Tableau, Markdown is an easy to learn tool for focusing on presenting data. However, for more controlled changes to the presentation and enhanced visuals, users need to know additional tools, including knitr, LaTex, Sweave, and RStudio. On the other hand, Tableau has a drag and drop interface that makes designing a breeze.
For example, consider simple charts and dashboards. All Tableau requires is data imports before following the steps needed to create a simple chart. From there, you can transport these charts into a dashboard without any lines of code. R Shiny, a web-based application used for R users’ data presentation, still requires coding knowledge for simple charts. Creating an R Shiny dashboard is relatively easy but requires more code for finer details and beautification.
Note: You may be wondering: if VizQL works off of SQL and products like R Shiny are R-focused, does that mean they are limited to data by programming language source? No. Both software can intake a multitude of data sources across many different languages. So when considering either service, this is not a factor that needs much attention.
2 Areas Tableau is Not Better than R
Capable Tasks and Toolset
Tableau’s greatest strength is also its greatest weakness. The software’s emphasis on user-friendly data visualizations leaves much to be desired when it comes to other tasks. Furthermore, it leaves room for disappointment if your design goals surpass a Tableau dashboard’s simple presentation.
First, think about the task load that R can take on. R’s job capabilities include web extraction, machine learning, statistics, data visualization, interactive reporting, and web-apps. That is a lot! Even though R requires the user to be literate in the language, the benefit is a suite of tasks covered by one coding language.
So what accomplishments can you achieve with R? Here are some examples from data science professional Ashutosh R Nandeshwar:
- Using visNetwork, Nandeshwar was able to create an interactive network map of Game of Thrones characters.
- What about animated data? R has you covered there too! Check out this animated graph displaying Walmart’s growth, also created by Nandeshwar:
- Another great point that Nandeshwar makes: R Shiny can develop web-apps. How cool is that!?
As you can see, R can do a lot of things. The software truly surpasses the label of a ‘statistician’s tool.’ It expands into a vast environment of tools that work hand in hand with each other. Even when it comes to Tableau’s visual presentation capabilities, R can exceed these parameters and create dazzling data presentation. If you are willing to put in the time and study, R will undoubtedly pay you back.
Tableau’s other drawback is cost. R, being an open-source language, is free. On the other hand, Tableau sells licenses dictated by role. You can buy a creator license (design and sharing ability), an explorer license (limited designing and sharing), or a viewer license (viewing access only). Purchasing and managing licenses can make data sharing between parties complicated and expensive.
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In short, I could not recommend Tableau over R, or vice-versa, because they are too different. Tableau is best for those who are not literate in programming languages. R benefits a programmer that knows what they are doing. However, you can still use them together, especially if you want to save time visualizing the data you calculated and store in R. However, you may want to consider how much money you wish to spend on Tableau.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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